Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f7a47dd8080>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f7a47d05898>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.
  

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    real_inputs = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels),
                                'real_inputs')
    z_inputs = tf.placeholder(tf.float32, (None, z_dim), 'z_inputs')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')

    return real_inputs, z_inputs, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [7]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    with tf.variable_scope('discriminator', reuse=reuse):
        
        alpha = 0.2
        
        h1 = tf.layers.conv2d(images, 64, 5, 2, 'same')
        h1 = tf.maximum(alpha * h1, h1)
        
        h2 = tf.layers.conv2d(h1, 128, 5, 2, 'same')
        h2 = tf.layers.batch_normalization(h2, training=True)
        h2 = tf.maximum(alpha * h2, h2)
        
        h3 = tf.layers.conv2d(h2, 256, 5, 2, 'same')
        h3 = tf.layers.batch_normalization(h3, training=True)
        h3 = tf.maximum(alpha * h3, h3)
        
        flat = tf.reshape(h3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        
    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [8]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    with tf.variable_scope('generator', reuse=not is_train):
        alpha = 0.2
    
        h1 = tf.layers.dense(z, 2*2*512)
        h1 = tf.reshape(h1, (-1, 2, 2, 512))
        h1 = tf.layers.batch_normalization(h1, training=is_train)
        h1 = tf.maximum(alpha * h1, h1)
    
        h2 = tf.layers.conv2d_transpose(h1, 256, 5, 2, 'valid')
        h2 = tf.layers.batch_normalization(h2, training=is_train)
        h2 = tf.maximum(alpha * h2, h2)
    
        h3 = tf.layers.conv2d_transpose(h2, 128, 5, 2, 'same')
        h3 = tf.layers.batch_normalization(h3, training=is_train)
        h3 = tf.maximum(alpha * h3, h3)
    
        logits = tf.layers.conv2d_transpose(h3, out_channel_dim, 5, 2, 'same')
        out = tf.tanh(logits)
    
        return out

    
    

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [12]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)

    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])

    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            steps = 0
            for batch_images in get_batches(batch_size):
                steps +=1
                batch_images = batch_images * 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                
                if steps % 10 == 0:
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Batch {}...".format(steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % 100 == 0:
                    show_generator_output(sess, show_n_images, input_z, data_shape[3], data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the lo ss of the discriminator or close to 0.

In [14]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Batch 10... Discriminator Loss: 0.2719... Generator Loss: 1.6796
Epoch 1/2... Batch 20... Discriminator Loss: 0.1821... Generator Loss: 2.1954
Epoch 1/2... Batch 30... Discriminator Loss: 0.0181... Generator Loss: 6.1200
Epoch 1/2... Batch 40... Discriminator Loss: 0.0237... Generator Loss: 5.2465
Epoch 1/2... Batch 50... Discriminator Loss: 0.2262... Generator Loss: 1.7704
Epoch 1/2... Batch 60... Discriminator Loss: 0.2105... Generator Loss: 1.8747
Epoch 1/2... Batch 70... Discriminator Loss: 0.1068... Generator Loss: 12.5871
Epoch 1/2... Batch 80... Discriminator Loss: 0.1623... Generator Loss: 2.2517
Epoch 1/2... Batch 90... Discriminator Loss: 0.3821... Generator Loss: 2.2160
Epoch 1/2... Batch 100... Discriminator Loss: 0.3192... Generator Loss: 2.6240
Epoch 1/2... Batch 110... Discriminator Loss: 0.2528... Generator Loss: 2.6554
Epoch 1/2... Batch 120... Discriminator Loss: 0.3774... Generator Loss: 5.1414
Epoch 1/2... Batch 130... Discriminator Loss: 0.7617... Generator Loss: 0.9765
Epoch 1/2... Batch 140... Discriminator Loss: 0.3477... Generator Loss: 1.7954
Epoch 1/2... Batch 150... Discriminator Loss: 0.1307... Generator Loss: 2.7152
Epoch 1/2... Batch 160... Discriminator Loss: 0.2248... Generator Loss: 2.4641
Epoch 1/2... Batch 170... Discriminator Loss: 0.2531... Generator Loss: 2.3505
Epoch 1/2... Batch 180... Discriminator Loss: 0.3127... Generator Loss: 1.8603
Epoch 1/2... Batch 190... Discriminator Loss: 0.2011... Generator Loss: 2.5537
Epoch 1/2... Batch 200... Discriminator Loss: 0.2006... Generator Loss: 3.5183
Epoch 1/2... Batch 210... Discriminator Loss: 0.1404... Generator Loss: 3.1186
Epoch 1/2... Batch 220... Discriminator Loss: 0.1519... Generator Loss: 2.5362
Epoch 1/2... Batch 230... Discriminator Loss: 0.1289... Generator Loss: 3.1923
Epoch 1/2... Batch 240... Discriminator Loss: 0.2214... Generator Loss: 2.7303
Epoch 1/2... Batch 250... Discriminator Loss: 0.2028... Generator Loss: 4.2531
Epoch 1/2... Batch 260... Discriminator Loss: 0.0347... Generator Loss: 5.7882
Epoch 1/2... Batch 270... Discriminator Loss: 0.1158... Generator Loss: 3.0140
Epoch 1/2... Batch 280... Discriminator Loss: 0.1121... Generator Loss: 3.0816
Epoch 1/2... Batch 290... Discriminator Loss: 0.0963... Generator Loss: 3.6766
Epoch 1/2... Batch 300... Discriminator Loss: 0.1018... Generator Loss: 3.1417
Epoch 1/2... Batch 310... Discriminator Loss: 0.1080... Generator Loss: 6.8089
Epoch 1/2... Batch 320... Discriminator Loss: 0.2096... Generator Loss: 2.0518
Epoch 1/2... Batch 330... Discriminator Loss: 0.1860... Generator Loss: 2.2237
Epoch 1/2... Batch 340... Discriminator Loss: 0.2156... Generator Loss: 2.2420
Epoch 1/2... Batch 350... Discriminator Loss: 0.1762... Generator Loss: 5.2296
Epoch 1/2... Batch 360... Discriminator Loss: 0.4661... Generator Loss: 2.1751
Epoch 1/2... Batch 370... Discriminator Loss: 0.1569... Generator Loss: 4.7203
Epoch 1/2... Batch 380... Discriminator Loss: 0.8048... Generator Loss: 1.2129
Epoch 1/2... Batch 390... Discriminator Loss: 0.2948... Generator Loss: 6.9679
Epoch 1/2... Batch 400... Discriminator Loss: 0.2954... Generator Loss: 2.4848
Epoch 1/2... Batch 410... Discriminator Loss: 0.2002... Generator Loss: 2.6276
Epoch 1/2... Batch 420... Discriminator Loss: 0.0743... Generator Loss: 3.5636
Epoch 1/2... Batch 430... Discriminator Loss: 0.3129... Generator Loss: 2.0000
Epoch 1/2... Batch 440... Discriminator Loss: 0.1810... Generator Loss: 3.8295
Epoch 1/2... Batch 450... Discriminator Loss: 0.7002... Generator Loss: 1.6921
Epoch 1/2... Batch 460... Discriminator Loss: 0.1121... Generator Loss: 2.8664
Epoch 1/2... Batch 470... Discriminator Loss: 0.3378... Generator Loss: 2.4967
Epoch 1/2... Batch 480... Discriminator Loss: 0.1376... Generator Loss: 3.7654
Epoch 1/2... Batch 490... Discriminator Loss: 0.1255... Generator Loss: 4.4691
Epoch 1/2... Batch 500... Discriminator Loss: 0.3422... Generator Loss: 2.7364
Epoch 1/2... Batch 510... Discriminator Loss: 0.3017... Generator Loss: 4.6931
Epoch 1/2... Batch 520... Discriminator Loss: 0.5420... Generator Loss: 1.6203
Epoch 1/2... Batch 530... Discriminator Loss: 0.3091... Generator Loss: 1.9637
Epoch 1/2... Batch 540... Discriminator Loss: 0.3474... Generator Loss: 2.0505
Epoch 1/2... Batch 550... Discriminator Loss: 0.2866... Generator Loss: 4.0659
Epoch 1/2... Batch 560... Discriminator Loss: 0.4606... Generator Loss: 1.4198
Epoch 1/2... Batch 570... Discriminator Loss: 0.2534... Generator Loss: 3.2763
Epoch 1/2... Batch 580... Discriminator Loss: 0.2976... Generator Loss: 3.1177
Epoch 1/2... Batch 590... Discriminator Loss: 0.3123... Generator Loss: 2.9420
Epoch 1/2... Batch 600... Discriminator Loss: 0.5399... Generator Loss: 2.4677
Epoch 1/2... Batch 610... Discriminator Loss: 0.5018... Generator Loss: 1.6669
Epoch 1/2... Batch 620... Discriminator Loss: 0.4821... Generator Loss: 2.6486
Epoch 1/2... Batch 630... Discriminator Loss: 0.5116... Generator Loss: 1.6479
Epoch 1/2... Batch 640... Discriminator Loss: 0.4188... Generator Loss: 2.4351
Epoch 1/2... Batch 650... Discriminator Loss: 0.7618... Generator Loss: 1.0470
Epoch 1/2... Batch 660... Discriminator Loss: 0.5873... Generator Loss: 1.4827
Epoch 1/2... Batch 670... Discriminator Loss: 0.4045... Generator Loss: 2.8636
Epoch 1/2... Batch 680... Discriminator Loss: 0.3332... Generator Loss: 2.0478
Epoch 1/2... Batch 690... Discriminator Loss: 0.5662... Generator Loss: 1.6123
Epoch 1/2... Batch 700... Discriminator Loss: 0.3431... Generator Loss: 2.3074
Epoch 1/2... Batch 710... Discriminator Loss: 0.5778... Generator Loss: 2.9973
Epoch 1/2... Batch 720... Discriminator Loss: 0.6525... Generator Loss: 1.5318
Epoch 1/2... Batch 730... Discriminator Loss: 0.5576... Generator Loss: 1.3074
Epoch 1/2... Batch 740... Discriminator Loss: 0.5539... Generator Loss: 3.9337
Epoch 1/2... Batch 750... Discriminator Loss: 0.7431... Generator Loss: 1.3005
Epoch 1/2... Batch 760... Discriminator Loss: 0.4585... Generator Loss: 2.0172
Epoch 1/2... Batch 770... Discriminator Loss: 0.5883... Generator Loss: 1.7040
Epoch 1/2... Batch 780... Discriminator Loss: 0.5490... Generator Loss: 1.6170
Epoch 1/2... Batch 790... Discriminator Loss: 0.5156... Generator Loss: 1.6573
Epoch 1/2... Batch 800... Discriminator Loss: 0.7192... Generator Loss: 1.0482
Epoch 1/2... Batch 810... Discriminator Loss: 0.3654... Generator Loss: 2.6091
Epoch 1/2... Batch 820... Discriminator Loss: 0.5281... Generator Loss: 1.4727
Epoch 1/2... Batch 830... Discriminator Loss: 0.9953... Generator Loss: 1.0682
Epoch 1/2... Batch 840... Discriminator Loss: 0.5370... Generator Loss: 1.7909
Epoch 1/2... Batch 850... Discriminator Loss: 0.4664... Generator Loss: 2.4115
Epoch 1/2... Batch 860... Discriminator Loss: 0.4457... Generator Loss: 2.0352
Epoch 1/2... Batch 870... Discriminator Loss: 0.8570... Generator Loss: 3.9938
Epoch 1/2... Batch 880... Discriminator Loss: 0.5002... Generator Loss: 1.7731
Epoch 1/2... Batch 890... Discriminator Loss: 0.5496... Generator Loss: 1.6237
Epoch 1/2... Batch 900... Discriminator Loss: 0.6241... Generator Loss: 2.8694
Epoch 1/2... Batch 910... Discriminator Loss: 0.3931... Generator Loss: 2.1368
Epoch 1/2... Batch 920... Discriminator Loss: 0.8105... Generator Loss: 0.9823
Epoch 1/2... Batch 930... Discriminator Loss: 0.4217... Generator Loss: 1.5681
Epoch 1/2... Batch 940... Discriminator Loss: 0.5676... Generator Loss: 3.1252
Epoch 1/2... Batch 950... Discriminator Loss: 0.7850... Generator Loss: 1.2887
Epoch 1/2... Batch 960... Discriminator Loss: 0.3920... Generator Loss: 2.0026
Epoch 1/2... Batch 970... Discriminator Loss: 0.5715... Generator Loss: 1.5387
Epoch 1/2... Batch 980... Discriminator Loss: 0.4506... Generator Loss: 1.7276
Epoch 1/2... Batch 990... Discriminator Loss: 0.7508... Generator Loss: 0.9715
Epoch 1/2... Batch 1000... Discriminator Loss: 0.8298... Generator Loss: 3.5046
Epoch 1/2... Batch 1010... Discriminator Loss: 1.0187... Generator Loss: 2.8856
Epoch 1/2... Batch 1020... Discriminator Loss: 0.5227... Generator Loss: 2.1817
Epoch 1/2... Batch 1030... Discriminator Loss: 0.4724... Generator Loss: 2.2303
Epoch 1/2... Batch 1040... Discriminator Loss: 0.4290... Generator Loss: 1.9234
Epoch 1/2... Batch 1050... Discriminator Loss: 1.0504... Generator Loss: 0.7502
Epoch 1/2... Batch 1060... Discriminator Loss: 0.5877... Generator Loss: 2.0202
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Epoch 2/2... Batch 1830... Discriminator Loss: 1.1248... Generator Loss: 0.5909
Epoch 2/2... Batch 1840... Discriminator Loss: 0.8849... Generator Loss: 0.7153
Epoch 2/2... Batch 1850... Discriminator Loss: 0.3587... Generator Loss: 1.5870
Epoch 2/2... Batch 1860... Discriminator Loss: 0.7800... Generator Loss: 1.0027
Epoch 2/2... Batch 1870... Discriminator Loss: 0.5294... Generator Loss: 1.1545

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 32
z_dim = 100
learning_rate = 0.0005
beta1 = 0.4



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Batch 10... Discriminator Loss: 0.3110... Generator Loss: 9.1354
Epoch 1/1... Batch 20... Discriminator Loss: 6.7563... Generator Loss: 0.0013
Epoch 1/1... Batch 30... Discriminator Loss: 0.4968... Generator Loss: 12.8739
Epoch 1/1... Batch 40... Discriminator Loss: 0.2970... Generator Loss: 7.8867
Epoch 1/1... Batch 50... Discriminator Loss: 0.6063... Generator Loss: 3.4185
Epoch 1/1... Batch 60... Discriminator Loss: 0.2388... Generator Loss: 8.2140
Epoch 1/1... Batch 70... Discriminator Loss: 1.8800... Generator Loss: 0.2802
Epoch 1/1... Batch 80... Discriminator Loss: 0.2924... Generator Loss: 3.4394
Epoch 1/1... Batch 90... Discriminator Loss: 0.4601... Generator Loss: 2.7617
Epoch 1/1... Batch 100... Discriminator Loss: 0.5517... Generator Loss: 1.4141
Epoch 1/1... Batch 110... Discriminator Loss: 0.7435... Generator Loss: 1.4346
Epoch 1/1... Batch 120... Discriminator Loss: 1.6069... Generator Loss: 0.3721
Epoch 1/1... Batch 130... Discriminator Loss: 0.4223... Generator Loss: 1.7585
Epoch 1/1... Batch 140... Discriminator Loss: 0.8646... Generator Loss: 0.7127
Epoch 1/1... Batch 150... Discriminator Loss: 1.6570... Generator Loss: 0.2964
Epoch 1/1... Batch 160... Discriminator Loss: 1.5331... Generator Loss: 4.5343
Epoch 1/1... Batch 170... Discriminator Loss: 1.4841... Generator Loss: 0.4269
Epoch 1/1... Batch 180... Discriminator Loss: 1.1957... Generator Loss: 3.0986
Epoch 1/1... Batch 190... Discriminator Loss: 1.2584... Generator Loss: 4.5331
Epoch 1/1... Batch 200... Discriminator Loss: 0.8126... Generator Loss: 1.4202
Epoch 1/1... Batch 210... Discriminator Loss: 0.9406... Generator Loss: 0.9084
Epoch 1/1... Batch 220... Discriminator Loss: 1.4838... Generator Loss: 0.7087
Epoch 1/1... Batch 230... Discriminator Loss: 1.2246... Generator Loss: 1.0353
Epoch 1/1... Batch 240... Discriminator Loss: 1.1054... Generator Loss: 0.7177
Epoch 1/1... Batch 250... Discriminator Loss: 1.1632... Generator Loss: 0.9298
Epoch 1/1... Batch 260... Discriminator Loss: 1.4023... Generator Loss: 1.8402
Epoch 1/1... Batch 270... Discriminator Loss: 1.5556... Generator Loss: 0.3411
Epoch 1/1... Batch 280... Discriminator Loss: 0.9127... Generator Loss: 0.9221
Epoch 1/1... Batch 290... Discriminator Loss: 1.1802... Generator Loss: 1.1804
Epoch 1/1... Batch 300... Discriminator Loss: 1.0287... Generator Loss: 0.8675
Epoch 1/1... Batch 310... Discriminator Loss: 1.3094... Generator Loss: 0.9267
Epoch 1/1... Batch 320... Discriminator Loss: 1.0669... Generator Loss: 0.5998
Epoch 1/1... Batch 330... Discriminator Loss: 0.8794... Generator Loss: 2.2609
Epoch 1/1... Batch 340... Discriminator Loss: 1.2932... Generator Loss: 1.2298
Epoch 1/1... Batch 350... Discriminator Loss: 1.0700... Generator Loss: 0.9653
Epoch 1/1... Batch 360... Discriminator Loss: 1.1771... Generator Loss: 1.2644
Epoch 1/1... Batch 370... Discriminator Loss: 1.1231... Generator Loss: 0.7738
Epoch 1/1... Batch 380... Discriminator Loss: 1.2281... Generator Loss: 0.5940
Epoch 1/1... Batch 390... Discriminator Loss: 0.9362... Generator Loss: 1.2091
Epoch 1/1... Batch 400... Discriminator Loss: 1.6296... Generator Loss: 0.2881
Epoch 1/1... Batch 410... Discriminator Loss: 0.9760... Generator Loss: 0.9444
Epoch 1/1... Batch 420... Discriminator Loss: 1.0908... Generator Loss: 1.5101
Epoch 1/1... Batch 430... Discriminator Loss: 1.1983... Generator Loss: 0.8013
Epoch 1/1... Batch 440... Discriminator Loss: 0.9155... Generator Loss: 1.3215
Epoch 1/1... Batch 450... Discriminator Loss: 1.0797... Generator Loss: 0.9859
Epoch 1/1... Batch 460... Discriminator Loss: 0.8749... Generator Loss: 1.1582
Epoch 1/1... Batch 470... Discriminator Loss: 1.2587... Generator Loss: 1.4068
Epoch 1/1... Batch 480... Discriminator Loss: 1.2098... Generator Loss: 0.5374
Epoch 1/1... Batch 490... Discriminator Loss: 1.3410... Generator Loss: 1.4057
Epoch 1/1... Batch 500... Discriminator Loss: 1.0868... Generator Loss: 0.6748
Epoch 1/1... Batch 510... Discriminator Loss: 0.9759... Generator Loss: 0.8003
Epoch 1/1... Batch 520... Discriminator Loss: 0.9800... Generator Loss: 0.7570
Epoch 1/1... Batch 530... Discriminator Loss: 0.9049... Generator Loss: 1.2495
Epoch 1/1... Batch 540... Discriminator Loss: 0.8201... Generator Loss: 1.0845
Epoch 1/1... Batch 550... Discriminator Loss: 1.0105... Generator Loss: 2.1938
Epoch 1/1... Batch 560... Discriminator Loss: 0.8647... Generator Loss: 0.9050
Epoch 1/1... Batch 570... Discriminator Loss: 1.3598... Generator Loss: 0.4783
Epoch 1/1... Batch 580... Discriminator Loss: 0.8347... Generator Loss: 1.7410
Epoch 1/1... Batch 590... Discriminator Loss: 0.9970... Generator Loss: 0.7876
Epoch 1/1... Batch 600... Discriminator Loss: 1.1180... Generator Loss: 1.0609
Epoch 1/1... Batch 610... Discriminator Loss: 1.1326... Generator Loss: 0.6303
Epoch 1/1... Batch 620... Discriminator Loss: 1.3847... Generator Loss: 0.4486
Epoch 1/1... Batch 630... Discriminator Loss: 1.2685... Generator Loss: 0.5520
Epoch 1/1... Batch 640... Discriminator Loss: 1.1357... Generator Loss: 0.6079
Epoch 1/1... Batch 650... Discriminator Loss: 1.3926... Generator Loss: 0.6685
Epoch 1/1... Batch 660... Discriminator Loss: 1.3485... Generator Loss: 1.2050
Epoch 1/1... Batch 670... Discriminator Loss: 1.2145... Generator Loss: 0.8306
Epoch 1/1... Batch 680... Discriminator Loss: 1.3268... Generator Loss: 2.9133
Epoch 1/1... Batch 690... Discriminator Loss: 1.2068... Generator Loss: 0.7478
Epoch 1/1... Batch 700... Discriminator Loss: 1.6376... Generator Loss: 0.3930
Epoch 1/1... Batch 710... Discriminator Loss: 1.0409... Generator Loss: 0.6890
Epoch 1/1... Batch 720... Discriminator Loss: 1.2408... Generator Loss: 0.5474
Epoch 1/1... Batch 730... Discriminator Loss: 1.6154... Generator Loss: 0.2937
Epoch 1/1... Batch 740... Discriminator Loss: 1.7477... Generator Loss: 0.3027
Epoch 1/1... Batch 750... Discriminator Loss: 0.9709... Generator Loss: 1.0208
Epoch 1/1... Batch 760... Discriminator Loss: 1.5065... Generator Loss: 0.3382
Epoch 1/1... Batch 770... Discriminator Loss: 1.1234... Generator Loss: 1.3711
Epoch 1/1... Batch 780... Discriminator Loss: 0.9960... Generator Loss: 1.1088
Epoch 1/1... Batch 790... Discriminator Loss: 1.2397... Generator Loss: 1.5143
Epoch 1/1... Batch 800... Discriminator Loss: 1.0680... Generator Loss: 1.5743
Epoch 1/1... Batch 810... Discriminator Loss: 1.4731... Generator Loss: 0.4168
Epoch 1/1... Batch 820... Discriminator Loss: 1.1950... Generator Loss: 1.3030
Epoch 1/1... Batch 830... Discriminator Loss: 1.2378... Generator Loss: 0.9944
Epoch 1/1... Batch 840... Discriminator Loss: 1.0444... Generator Loss: 1.1106
Epoch 1/1... Batch 850... Discriminator Loss: 1.0541... Generator Loss: 0.9132
Epoch 1/1... Batch 860... Discriminator Loss: 1.7867... Generator Loss: 0.2229
Epoch 1/1... Batch 870... Discriminator Loss: 1.3488... Generator Loss: 1.7909
Epoch 1/1... Batch 880... Discriminator Loss: 1.1894... Generator Loss: 0.6704
Epoch 1/1... Batch 890... Discriminator Loss: 1.0332... Generator Loss: 0.8250
Epoch 1/1... Batch 900... Discriminator Loss: 1.1032... Generator Loss: 1.0482
Epoch 1/1... Batch 910... Discriminator Loss: 1.2174... Generator Loss: 0.6974
Epoch 1/1... Batch 920... Discriminator Loss: 1.1727... Generator Loss: 0.6241
Epoch 1/1... Batch 930... Discriminator Loss: 1.2258... Generator Loss: 0.6609
Epoch 1/1... Batch 940... Discriminator Loss: 1.0778... Generator Loss: 0.7923
Epoch 1/1... Batch 950... Discriminator Loss: 1.2248... Generator Loss: 0.6156
Epoch 1/1... Batch 960... Discriminator Loss: 1.6178... Generator Loss: 2.0239
Epoch 1/1... Batch 970... Discriminator Loss: 1.0358... Generator Loss: 0.8555
Epoch 1/1... Batch 980... Discriminator Loss: 1.0905... Generator Loss: 0.8118
Epoch 1/1... Batch 990... Discriminator Loss: 1.2440... Generator Loss: 0.5831
Epoch 1/1... Batch 1000... Discriminator Loss: 1.1270... Generator Loss: 0.8353
Epoch 1/1... Batch 1010... Discriminator Loss: 1.0084... Generator Loss: 0.9271
Epoch 1/1... Batch 1020... Discriminator Loss: 0.8861... Generator Loss: 1.1745
Epoch 1/1... Batch 1030... Discriminator Loss: 1.1706... Generator Loss: 0.8723
Epoch 1/1... Batch 1040... Discriminator Loss: 1.0891... Generator Loss: 0.9036
Epoch 1/1... Batch 1050... Discriminator Loss: 1.0433... Generator Loss: 0.8749
Epoch 1/1... Batch 1060... Discriminator Loss: 1.1875... Generator Loss: 0.7037
Epoch 1/1... Batch 1070... Discriminator Loss: 1.3203... Generator Loss: 0.4176
Epoch 1/1... Batch 1080... Discriminator Loss: 1.3287... Generator Loss: 0.4564
Epoch 1/1... Batch 1090... Discriminator Loss: 1.3177... Generator Loss: 0.5993
Epoch 1/1... Batch 1100... Discriminator Loss: 1.0916... Generator Loss: 0.6400
Epoch 1/1... Batch 1110... Discriminator Loss: 1.2620... Generator Loss: 1.5506
Epoch 1/1... Batch 1120... Discriminator Loss: 1.5509... Generator Loss: 0.3266
Epoch 1/1... Batch 1130... Discriminator Loss: 1.1685... Generator Loss: 0.8958
Epoch 1/1... Batch 1140... Discriminator Loss: 1.3449... Generator Loss: 0.6855
Epoch 1/1... Batch 1150... Discriminator Loss: 1.2794... Generator Loss: 0.5156
Epoch 1/1... Batch 1160... Discriminator Loss: 1.1311... Generator Loss: 1.2221
Epoch 1/1... Batch 1170... Discriminator Loss: 1.0159... Generator Loss: 0.8559
Epoch 1/1... Batch 1180... Discriminator Loss: 1.0482... Generator Loss: 0.6710
Epoch 1/1... Batch 1190... Discriminator Loss: 0.8822... Generator Loss: 0.9679
Epoch 1/1... Batch 1200... Discriminator Loss: 1.2067... Generator Loss: 0.5071
Epoch 1/1... Batch 1210... Discriminator Loss: 1.2464... Generator Loss: 0.5450
Epoch 1/1... Batch 1220... Discriminator Loss: 1.1315... Generator Loss: 0.9289
Epoch 1/1... Batch 1230... Discriminator Loss: 1.3319... Generator Loss: 0.4318
Epoch 1/1... Batch 1240... Discriminator Loss: 1.2473... Generator Loss: 2.4176
Epoch 1/1... Batch 1250... Discriminator Loss: 0.8499... Generator Loss: 1.3376
Epoch 1/1... Batch 1260... Discriminator Loss: 1.3956... Generator Loss: 0.3822
Epoch 1/1... Batch 1270... Discriminator Loss: 1.1554... Generator Loss: 0.7764
Epoch 1/1... Batch 1280... Discriminator Loss: 1.3381... Generator Loss: 0.5700
Epoch 1/1... Batch 1290... Discriminator Loss: 1.2025... Generator Loss: 1.0278
Epoch 1/1... Batch 1300... Discriminator Loss: 0.8985... Generator Loss: 0.8624
Epoch 1/1... Batch 1310... Discriminator Loss: 1.2559... Generator Loss: 0.6788
Epoch 1/1... Batch 1320... Discriminator Loss: 1.0776... Generator Loss: 0.7281
Epoch 1/1... Batch 1330... Discriminator Loss: 1.0266... Generator Loss: 0.8313
Epoch 1/1... Batch 1340... Discriminator Loss: 0.9670... Generator Loss: 1.1423
Epoch 1/1... Batch 1350... Discriminator Loss: 1.0040... Generator Loss: 0.8435
Epoch 1/1... Batch 1840... Discriminator Loss: 1.0203... Generator Loss: 1.0640
Epoch 1/1... Batch 1850... Discriminator Loss: 0.8270... Generator Loss: 1.0765
Epoch 1/1... Batch 1860... Discriminator Loss: 0.8485... Generator Loss: 0.9882
Epoch 1/1... Batch 1870... Discriminator Loss: 1.1603... Generator Loss: 0.5828
Epoch 1/1... Batch 1880... Discriminator Loss: 1.9038... Generator Loss: 0.2246
Epoch 1/1... Batch 1890... Discriminator Loss: 1.2613... Generator Loss: 0.9147
Epoch 1/1... Batch 1900... Discriminator Loss: 1.1237... Generator Loss: 0.7577
Epoch 1/1... Batch 1910... Discriminator Loss: 1.2716... Generator Loss: 0.4721
Epoch 1/1... Batch 1920... Discriminator Loss: 1.0805... Generator Loss: 0.6649
Epoch 1/1... Batch 1930... Discriminator Loss: 1.2229... Generator Loss: 0.5429
Epoch 1/1... Batch 1940... Discriminator Loss: 1.3233... Generator Loss: 0.6220
Epoch 1/1... Batch 1950... Discriminator Loss: 1.1822... Generator Loss: 0.7322
Epoch 1/1... Batch 1960... Discriminator Loss: 0.7833... Generator Loss: 1.1416
Epoch 1/1... Batch 1970... Discriminator Loss: 1.1524... Generator Loss: 0.7525
Epoch 1/1... Batch 1980... Discriminator Loss: 0.9980... Generator Loss: 0.9004
Epoch 1/1... Batch 1990... Discriminator Loss: 1.0451... Generator Loss: 0.9126
Epoch 1/1... Batch 2000... Discriminator Loss: 0.9003... Generator Loss: 0.9288
Epoch 1/1... Batch 2010... Discriminator Loss: 0.9764... Generator Loss: 1.0487
Epoch 1/1... Batch 2020... Discriminator Loss: 1.2150... Generator Loss: 0.5476
Epoch 1/1... Batch 2030... Discriminator Loss: 1.9070... Generator Loss: 0.2320
Epoch 1/1... Batch 2040... Discriminator Loss: 1.0504... Generator Loss: 0.7232
Epoch 1/1... Batch 2050... Discriminator Loss: 1.4357... Generator Loss: 0.4311
Epoch 1/1... Batch 2060... Discriminator Loss: 1.3673... Generator Loss: 0.4272
Epoch 1/1... Batch 2070... Discriminator Loss: 1.1976... Generator Loss: 0.6266
Epoch 1/1... Batch 2080... Discriminator Loss: 1.0615... Generator Loss: 1.0222
Epoch 1/1... Batch 2090... Discriminator Loss: 1.0714... Generator Loss: 0.9873
Epoch 1/1... Batch 2100... Discriminator Loss: 1.1633... Generator Loss: 0.5846
Epoch 1/1... Batch 2110... Discriminator Loss: 1.0081... Generator Loss: 1.7416
Epoch 1/1... Batch 2120... Discriminator Loss: 1.2019... Generator Loss: 0.7572
Epoch 1/1... Batch 2130... Discriminator Loss: 1.1790... Generator Loss: 0.5343
Epoch 1/1... Batch 2140... Discriminator Loss: 1.1587... Generator Loss: 0.6270
Epoch 1/1... Batch 2150... Discriminator Loss: 1.2817... Generator Loss: 0.7841
Epoch 1/1... Batch 2160... Discriminator Loss: 1.0076... Generator Loss: 0.8307
Epoch 1/1... Batch 2170... Discriminator Loss: 1.2899... Generator Loss: 0.8507
Epoch 1/1... Batch 2180... Discriminator Loss: 0.8976... Generator Loss: 1.2031
Epoch 1/1... Batch 2190... Discriminator Loss: 0.9315... Generator Loss: 1.1411
Epoch 1/1... Batch 2200... Discriminator Loss: 1.1827... Generator Loss: 1.5470
Epoch 1/1... Batch 2210... Discriminator Loss: 1.0950... Generator Loss: 0.6288
Epoch 1/1... Batch 2220... Discriminator Loss: 1.3684... Generator Loss: 0.4351
Epoch 1/1... Batch 2230... Discriminator Loss: 1.3212... Generator Loss: 1.1327
Epoch 1/1... Batch 2240... Discriminator Loss: 1.5603... Generator Loss: 0.3124
Epoch 1/1... Batch 2250... Discriminator Loss: 1.4676... Generator Loss: 0.4280
Epoch 1/1... Batch 2260... Discriminator Loss: 1.0531... Generator Loss: 0.8350
Epoch 1/1... Batch 2270... Discriminator Loss: 0.9979... Generator Loss: 0.8527
Epoch 1/1... Batch 2280... Discriminator Loss: 1.0858... Generator Loss: 0.9602
Epoch 1/1... Batch 2290... Discriminator Loss: 1.2619... Generator Loss: 0.4664
Epoch 1/1... Batch 2300... Discriminator Loss: 0.9882... Generator Loss: 0.9068
Epoch 1/1... Batch 2310... Discriminator Loss: 0.8624... Generator Loss: 1.4041
Epoch 1/1... Batch 2320... Discriminator Loss: 0.8669... Generator Loss: 1.2075
Epoch 1/1... Batch 2330... Discriminator Loss: 1.2328... Generator Loss: 0.9215
Epoch 1/1... Batch 2340... Discriminator Loss: 1.2772... Generator Loss: 0.6190
Epoch 1/1... Batch 2350... Discriminator Loss: 0.9442... Generator Loss: 1.2134
Epoch 1/1... Batch 2360... Discriminator Loss: 1.0627... Generator Loss: 1.3521
Epoch 1/1... Batch 2370... Discriminator Loss: 0.9581... Generator Loss: 1.0047
Epoch 1/1... Batch 2380... Discriminator Loss: 1.3579... Generator Loss: 0.5460
Epoch 1/1... Batch 2390... Discriminator Loss: 1.0514... Generator Loss: 0.5918
Epoch 1/1... Batch 2400... Discriminator Loss: 1.3968... Generator Loss: 2.2068
Epoch 1/1... Batch 2410... Discriminator Loss: 1.2330... Generator Loss: 0.9670
Epoch 1/1... Batch 2420... Discriminator Loss: 0.9682... Generator Loss: 1.0940
Epoch 1/1... Batch 2430... Discriminator Loss: 1.2227... Generator Loss: 0.8509
Epoch 1/1... Batch 2440... Discriminator Loss: 1.3011... Generator Loss: 0.7100
Epoch 1/1... Batch 2450... Discriminator Loss: 1.1981... Generator Loss: 0.5000
Epoch 1/1... Batch 2460... Discriminator Loss: 1.1067... Generator Loss: 1.0845
Epoch 1/1... Batch 2470... Discriminator Loss: 1.0680... Generator Loss: 1.1061
Epoch 1/1... Batch 2480... Discriminator Loss: 0.9447... Generator Loss: 0.8948
Epoch 1/1... Batch 2490... Discriminator Loss: 0.9571... Generator Loss: 0.7850
Epoch 1/1... Batch 2500... Discriminator Loss: 0.8253... Generator Loss: 1.1251
Epoch 1/1... Batch 2510... Discriminator Loss: 1.0162... Generator Loss: 1.2244
Epoch 1/1... Batch 2520... Discriminator Loss: 1.1890... Generator Loss: 0.8801
Epoch 1/1... Batch 2530... Discriminator Loss: 0.9988... Generator Loss: 0.7355
Epoch 1/1... Batch 2540... Discriminator Loss: 1.1720... Generator Loss: 0.7059
Epoch 1/1... Batch 2550... Discriminator Loss: 1.1921... Generator Loss: 0.7006
Epoch 1/1... Batch 2560... Discriminator Loss: 1.3699... Generator Loss: 0.4008
Epoch 1/1... Batch 2570... Discriminator Loss: 1.2286... Generator Loss: 1.2856
Epoch 1/1... Batch 2580... Discriminator Loss: 1.0424... Generator Loss: 0.8543
Epoch 1/1... Batch 2590... Discriminator Loss: 1.0703... Generator Loss: 0.9196
Epoch 1/1... Batch 2600... Discriminator Loss: 1.4130... Generator Loss: 0.4818
Epoch 1/1... Batch 2610... Discriminator Loss: 1.0603... Generator Loss: 0.6379
Epoch 1/1... Batch 2620... Discriminator Loss: 1.2353... Generator Loss: 0.8359
Epoch 1/1... Batch 2630... Discriminator Loss: 0.8299... Generator Loss: 1.0894
Epoch 1/1... Batch 2640... Discriminator Loss: 1.1621... Generator Loss: 0.6317
Epoch 1/1... Batch 2650... Discriminator Loss: 1.1555... Generator Loss: 0.5699
Epoch 1/1... Batch 2660... Discriminator Loss: 0.9517... Generator Loss: 1.1224
Epoch 1/1... Batch 2670... Discriminator Loss: 1.1041... Generator Loss: 1.1949
Epoch 1/1... Batch 2680... Discriminator Loss: 1.0787... Generator Loss: 0.6486
Epoch 1/1... Batch 2690... Discriminator Loss: 0.9807... Generator Loss: 0.7802
Epoch 1/1... Batch 2700... Discriminator Loss: 1.1551... Generator Loss: 0.6217
Epoch 1/1... Batch 2710... Discriminator Loss: 1.0169... Generator Loss: 0.8654
Epoch 1/1... Batch 2720... Discriminator Loss: 1.0487... Generator Loss: 0.6470
Epoch 1/1... Batch 2730... Discriminator Loss: 0.8512... Generator Loss: 1.2126
Epoch 1/1... Batch 2740... Discriminator Loss: 1.1513... Generator Loss: 1.1510
Epoch 1/1... Batch 2750... Discriminator Loss: 1.0919... Generator Loss: 0.7312
Epoch 1/1... Batch 2760... Discriminator Loss: 0.8808... Generator Loss: 1.2426
Epoch 1/1... Batch 2770... Discriminator Loss: 1.0391... Generator Loss: 0.8711
Epoch 1/1... Batch 2780... Discriminator Loss: 1.2152... Generator Loss: 1.0945
Epoch 1/1... Batch 2790... Discriminator Loss: 1.0051... Generator Loss: 1.0017
Epoch 1/1... Batch 2800... Discriminator Loss: 1.2606... Generator Loss: 0.6260
Epoch 1/1... Batch 2810... Discriminator Loss: 1.1756... Generator Loss: 0.6619
Epoch 1/1... Batch 2820... Discriminator Loss: 1.1275... Generator Loss: 0.5748
Epoch 1/1... Batch 2830... Discriminator Loss: 1.3180... Generator Loss: 0.5432
Epoch 1/1... Batch 2840... Discriminator Loss: 1.1522... Generator Loss: 0.6005
Epoch 1/1... Batch 2850... Discriminator Loss: 0.9441... Generator Loss: 0.8341
Epoch 1/1... Batch 2860... Discriminator Loss: 0.9966... Generator Loss: 0.7552
Epoch 1/1... Batch 2870... Discriminator Loss: 0.9290... Generator Loss: 0.9071
Epoch 1/1... Batch 2880... Discriminator Loss: 1.2763... Generator Loss: 0.6124
Epoch 1/1... Batch 2890... Discriminator Loss: 1.1751... Generator Loss: 0.8646
Epoch 1/1... Batch 2900... Discriminator Loss: 1.0111... Generator Loss: 1.0192
Epoch 1/1... Batch 2910... Discriminator Loss: 1.1139... Generator Loss: 0.8475
Epoch 1/1... Batch 2920... Discriminator Loss: 0.9596... Generator Loss: 0.9819
Epoch 1/1... Batch 2930... Discriminator Loss: 0.8524... Generator Loss: 1.1181
Epoch 1/1... Batch 2940... Discriminator Loss: 1.1784... Generator Loss: 1.3179
Epoch 1/1... Batch 2950... Discriminator Loss: 0.7260... Generator Loss: 1.8336
Epoch 1/1... Batch 2960... Discriminator Loss: 1.0154... Generator Loss: 0.7191
Epoch 1/1... Batch 2970... Discriminator Loss: 0.9833... Generator Loss: 0.8241
Epoch 1/1... Batch 2980... Discriminator Loss: 0.8804... Generator Loss: 0.9467
Epoch 1/1... Batch 2990... Discriminator Loss: 1.2350... Generator Loss: 0.6852
Epoch 1/1... Batch 3000... Discriminator Loss: 0.8387... Generator Loss: 1.3016
Epoch 1/1... Batch 3010... Discriminator Loss: 0.9690... Generator Loss: 0.8920
Epoch 1/1... Batch 3020... Discriminator Loss: 0.9484... Generator Loss: 1.3015
Epoch 1/1... Batch 3030... Discriminator Loss: 1.0008... Generator Loss: 0.9446
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Epoch 1/1... Batch 5670... Discriminator Loss: 1.1277... Generator Loss: 0.9336
Epoch 1/1... Batch 5680... Discriminator Loss: 1.2135... Generator Loss: 0.4609
Epoch 1/1... Batch 5690... Discriminator Loss: 1.1909... Generator Loss: 0.8836
Epoch 1/1... Batch 5700... Discriminator Loss: 1.0897... Generator Loss: 0.6088
Epoch 1/1... Batch 5710... Discriminator Loss: 1.0500... Generator Loss: 0.8440
Epoch 1/1... Batch 5720... Discriminator Loss: 1.2564... Generator Loss: 0.4785
Epoch 1/1... Batch 5730... Discriminator Loss: 0.9468... Generator Loss: 0.7637
Epoch 1/1... Batch 5740... Discriminator Loss: 0.9414... Generator Loss: 0.8353
Epoch 1/1... Batch 5750... Discriminator Loss: 0.9178... Generator Loss: 0.7949
Epoch 1/1... Batch 5760... Discriminator Loss: 1.0624... Generator Loss: 0.6925
Epoch 1/1... Batch 5770... Discriminator Loss: 0.9010... Generator Loss: 1.0607
Epoch 1/1... Batch 5780... Discriminator Loss: 1.0114... Generator Loss: 0.7783
Epoch 1/1... Batch 5790... Discriminator Loss: 0.9098... Generator Loss: 1.0094
Epoch 1/1... Batch 5800... Discriminator Loss: 0.9880... Generator Loss: 0.7406
Epoch 1/1... Batch 5810... Discriminator Loss: 1.3852... Generator Loss: 0.4123
Epoch 1/1... Batch 5820... Discriminator Loss: 1.0762... Generator Loss: 0.7392
Epoch 1/1... Batch 5830... Discriminator Loss: 1.0451... Generator Loss: 0.7736
Epoch 1/1... Batch 5840... Discriminator Loss: 1.2969... Generator Loss: 0.3962
Epoch 1/1... Batch 5850... Discriminator Loss: 1.5106... Generator Loss: 1.2604
Epoch 1/1... Batch 5860... Discriminator Loss: 1.4091... Generator Loss: 0.3647
Epoch 1/1... Batch 5870... Discriminator Loss: 1.2056... Generator Loss: 0.6031
Epoch 1/1... Batch 5880... Discriminator Loss: 0.7734... Generator Loss: 1.0446
Epoch 1/1... Batch 5890... Discriminator Loss: 1.4157... Generator Loss: 0.4241
Epoch 1/1... Batch 5900... Discriminator Loss: 1.0634... Generator Loss: 0.9692
Epoch 1/1... Batch 5910... Discriminator Loss: 1.0970... Generator Loss: 0.8037
Epoch 1/1... Batch 5920... Discriminator Loss: 1.0253... Generator Loss: 0.8561
Epoch 1/1... Batch 5930... Discriminator Loss: 1.1965... Generator Loss: 0.6572
Epoch 1/1... Batch 5940... Discriminator Loss: 1.3752... Generator Loss: 1.0000
Epoch 1/1... Batch 5950... Discriminator Loss: 1.0224... Generator Loss: 0.7001
Epoch 1/1... Batch 5960... Discriminator Loss: 1.4989... Generator Loss: 0.3406
Epoch 1/1... Batch 5970... Discriminator Loss: 1.0087... Generator Loss: 1.0397
Epoch 1/1... Batch 5980... Discriminator Loss: 1.2464... Generator Loss: 0.4845
Epoch 1/1... Batch 5990... Discriminator Loss: 1.0830... Generator Loss: 0.5966
Epoch 1/1... Batch 6000... Discriminator Loss: 1.5265... Generator Loss: 2.0764
Epoch 1/1... Batch 6010... Discriminator Loss: 1.1790... Generator Loss: 0.5868
Epoch 1/1... Batch 6020... Discriminator Loss: 0.8796... Generator Loss: 0.8824
Epoch 1/1... Batch 6030... Discriminator Loss: 1.0533... Generator Loss: 0.8869
Epoch 1/1... Batch 6040... Discriminator Loss: 1.1332... Generator Loss: 1.1462
Epoch 1/1... Batch 6050... Discriminator Loss: 1.3197... Generator Loss: 0.4343
Epoch 1/1... Batch 6060... Discriminator Loss: 1.0050... Generator Loss: 0.9696
Epoch 1/1... Batch 6070... Discriminator Loss: 1.2356... Generator Loss: 0.5974
Epoch 1/1... Batch 6080... Discriminator Loss: 1.1207... Generator Loss: 1.1644
Epoch 1/1... Batch 6090... Discriminator Loss: 1.0520... Generator Loss: 0.6681
Epoch 1/1... Batch 6100... Discriminator Loss: 1.2888... Generator Loss: 1.5087
Epoch 1/1... Batch 6110... Discriminator Loss: 1.1585... Generator Loss: 0.8316
Epoch 1/1... Batch 6120... Discriminator Loss: 1.2639... Generator Loss: 0.5035
Epoch 1/1... Batch 6130... Discriminator Loss: 1.1674... Generator Loss: 0.8716
Epoch 1/1... Batch 6140... Discriminator Loss: 0.9843... Generator Loss: 1.0100
Epoch 1/1... Batch 6150... Discriminator Loss: 0.9508... Generator Loss: 0.9604
Epoch 1/1... Batch 6160... Discriminator Loss: 0.8493... Generator Loss: 0.9437
Epoch 1/1... Batch 6170... Discriminator Loss: 1.0651... Generator Loss: 0.7960
Epoch 1/1... Batch 6180... Discriminator Loss: 1.1330... Generator Loss: 0.5859
Epoch 1/1... Batch 6190... Discriminator Loss: 1.1312... Generator Loss: 0.6144
Epoch 1/1... Batch 6200... Discriminator Loss: 0.9818... Generator Loss: 1.0476
Epoch 1/1... Batch 6210... Discriminator Loss: 1.2987... Generator Loss: 0.4233
Epoch 1/1... Batch 6220... Discriminator Loss: 0.9549... Generator Loss: 1.3589
Epoch 1/1... Batch 6230... Discriminator Loss: 1.7784... Generator Loss: 0.2706
Epoch 1/1... Batch 6240... Discriminator Loss: 0.8930... Generator Loss: 1.0811
Epoch 1/1... Batch 6250... Discriminator Loss: 1.4226... Generator Loss: 0.6291
Epoch 1/1... Batch 6260... Discriminator Loss: 1.1656... Generator Loss: 0.7475
Epoch 1/1... Batch 6270... Discriminator Loss: 1.1043... Generator Loss: 1.6619
Epoch 1/1... Batch 6280... Discriminator Loss: 1.2269... Generator Loss: 0.8272
Epoch 1/1... Batch 6290... Discriminator Loss: 1.2066... Generator Loss: 0.8573
Epoch 1/1... Batch 6300... Discriminator Loss: 1.5737... Generator Loss: 0.3122
Epoch 1/1... Batch 6310... Discriminator Loss: 1.3498... Generator Loss: 0.4752
Epoch 1/1... Batch 6320... Discriminator Loss: 1.0454... Generator Loss: 0.7067
Epoch 1/1... Batch 6330... Discriminator Loss: 1.4267... Generator Loss: 0.3698

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.